import os
import random
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
import torch.optim as optim
from matplotlib import pyplot as plt
from model.lenet import LeNet, LeNetSequetial
from tools.my_dataset import RMBDataset
from torch.utils.tensorboard import SummaryWriter

import sys
sys.path.append('..')
from enviroments import rmb_split_dir

# 待测试训练结果是否可以复现
def set_seed(seed = 1):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

set_seed()
# rmb_label = {"1":0, "100": 1}

# # 参数设置
# MAX_EPOCH = 10
# BATCH_SIZE = 16
# LR = 0.01
# log_interval = 10
# val_interval = 1

# # 设置路径参数
# train_dir = os.path.join(rmb_split_dir, "train")
# valid_dir = os.path.join(rmb_split_dir, "valid")

# # 待计算
# norm_mean = [0.485, 0.456, 0.406]
# norm_std = [0.229, 0.224, 0.225]
# # 设置训练集的数据增强和转化
# train_
